SoroushVahidi/scidocs huggingface dataset:
A natural language planner interface for mobile manipulators
(2014)

Natural language interfaces for robot control aspire to find the best sequence of actions that reflect the behavior intended by the instruction. This is difficult because of the diversity of language, variety of environments, and heterogeneity of tasks. Previous work has demonstrated that probabilistic graphical models constructed from the parse structure of natural language can be used to identify motions that most closely resemble verb phrases. Such approaches however quickly succumb to computational bottlenecks imposed by construction and search the space of possible actions. Planning constraints, which define goal regions and separate the admissible and inadmissible states in an environment model, provide an interesting alternative to represent the meaning of verb phrases. In this paper we present a new model called the Distributed Correspondence Graph (DCG) to infer the most likely set of planning constraints from natural language instructions. A trajectory planner then uses these planning constraints to find a sequence of actions that resemble the instruction. Separating the problem of identifying the action encoded by the language into individual steps of planning constraint inference and motion planning enables us to avoid computational costs associated with generation and evaluation of many trajectories. We present experimental results from comparative experiments that demonstrate improvements in efficiency in natural language understanding without loss of accuracy.
The Myth of Millions of Annual Self-Defense Gun Uses: A Case Study of Survey Overestimates of Rare Events
(1997)

A criminologist has been using national self-report surveys to estimate the incidence of self-defense gun use in the United States (Kleck 199 I). His most recent estimate is that civilians use guns in self-defense against offenders more than 2.5 million times each year (Kleck and Gertz 1995). This figure has been widely cited, not only by the National Rifle Association but in the media and in Congress. A criminologist in Canada is touting high self-defense estimates for that country, derived from similar self-report surveys (Mauser 1995). All attempts at external validation of the 2.5 million figure show that it is an enormous overestimate (Hemenway, in press). For example, in 34% of the times a gun was used for self-defense, the offender was allegedlycommitting a burglary. In other words, guns were reportedly used by defenders for self-defense in approximately 845,000 burglaries. From sophisticated victimization surveys, however,we know that there were fewer than 6 million burglaries in the year of the survey and in only 22% of those cases was someone certainly at home (1.3 million burglaries). Since only 42% of U.S. households own firearms,
One Century of Brain Mapping Using Brodmann Areas*
(2009)

100 years after their publication, Brodmann’s maps of the cerebral cortex are universally used to locate neuropsychological functions. On the occasion of this jubilee the life and work of Korbinian Brodmann are reported. The core functions of each single Brodmann area are described and Brodmann’s views on neuropsychological processes are depicted. 100 Jahre nach ihrer Veröffentlichung wird Brodmanns Kartierung des zerebralen Kortex universell zur Lokalisation neuropsychologischer Funktionen eingesetzt. Anlässlich dieses Jubiläums werden Leben und Werk von Korbinian Brodmann dargestellt. Die wesentlichen Funktionen der einzelnen Brodmann-Areale werden beschrieben und Brodmanns Ansichten über neuropsychologische Prozesse wiedergegeben.
CONTEXTUAL CLASSIFICATION OF POINT CLOUD DATA BY EXPLOITING INDIVIDUAL 3D NEIGBOURHOODS
(2015)

The fully automated analysis of 3D point clouds is of great importance in photogrammetry, remote sensing and computer vision. For reliably extracting objects such as buildings, road inventory or vegetation, many approaches rely on the results of a point cloud classification, where each 3D point is assigned a respective semantic class label. Such an assignment, in turn, typically involves statistical methods for feature extraction and machine learning. Whereas the different components in the processing workflow have extensively, but separately been investigated in recent years, the respective connection by sharing the results of crucial tasks across all components has not yet been addressed. This connection not only encapsulates the interrelated issues of neighborhood selection and feature extraction, but also the issue of how to involve spatial context in the classification step. In this paper, we present a novel and generic approach for 3D scene analysis which relies on (i) individually optimized 3D neighborhoods for (ii) the extraction of distinctive geometric features and (iii) the contextual classification of point cloud data. For a labeled benchmark dataset, we demonstrate the beneficial impact of involving contextual information in the classification process and that using individual 3D neighborhoods of optimal size significantly increases the quality of the results for both pointwise and contextual classification.
Examining the factors that influence early adopters' smartphone adoption: The case of college students
(2014)

The influence of early adopters on potential adopters’ decisions of whether or not to adopt a product is known to be critical. In this paper, we examine the factors that influence the adoption behavior of smartphone early adopters by looking at smartphone adoption behavior of college students, because a large portion of the early adopters of smartphones are college students. Our focus is on the effect of normative peer influence on a college student’s smartphone adoption. We also examine the influence of other factors such as selfinnovativeness, self-efficacy, the decision maker’s attitudes towards a product, financial burden of using the product, familial influence, and other demographic factors (e.g., age and gender). College students’ adoption behavior is studied using logit and probit choice models developed based on random utility theory. The discrete choice models are empirically estimated using survey data. We find important influence of friends, financial burden, and other family members on the smartphone adoption of college students who adopted smartphones earlier than other students. 2013 Elsevier Ltd. All rights reserved.
The calvaria of Sangiran 38, Sendangbusik, Sangiran Dome, Java.
(2010)

We describe in detail Sangiran 38 (S38), an adult partial calvaria recovered in 1980 from the Bapang (Kabuh) Formation of the Sangiran Dome near the hamlet of Sendangbusik, Java. Several other hominins (Bukuran, Hanoman 1, and Bs 9706) recovered in the vicinity come from either the upper-most Sangiran (Pucangan) or lower-most Bapang formations. S38 is from the lower Bapang Formation, which (40)Ar/(39)Ar age estimates suggest spans between 1.47 and 1.58Ma. Anatomical and metric comparisons with a worldwide set of 'early non-erectus'Homo, and Homo erectus (sensu lato) fossils indicate S38 is best considered a member of H. erectus. Although smaller in size, S38 is similar in overall morphology to the Bukuran specimen of similar age and provenance. The S38 calvaria exhibits several depressed lesions of the vault consistent with a scalp or systemic infection or soft tissue cyst.
Using Discriminant Eigenfeatures for Image Retrieval
(1996)

|This paper describes the automatic selection of features from an image training set using the theories of multi-dimensional linear discriminant analysis and the associated optimal linear projection. We demonstrate the eeectiveness of these Most Discriminating Features for view-based class retrieval from a large database of widely varying real-world objects presented as \well-framed" views, and compare it with that of the principal component analysis.
A Novel Economic Sharing Model in a Federation of Selfish Cloud Providers
(2014)

This paper presents a novel economic model to regulate capacity sharing in a federation of hybrid cloud providers (CPs). The proposed work models the interactions among the CPs as a repeated game among selfish players that aim at maximizing their profit by selling their unused capacity in the spot market but are uncertain of future workload fluctuations. The proposed work first establishes that the uncertainty in future revenue can act as a participation incentive to sharing in the repeated game. We, then, demonstrate how an efficient sharing strategy can be obtained via solving a simple dynamic programming problem. The obtained strategy is a simple update rule that depends only on the current workloads and a single variable summarizing past interactions. In contrast to existing approaches, the model incorporates historical and expected future revenue as part of the virtual machine (VM) sharing decision. Moreover, these decisions are not enforced neither by a centralized broker nor by predefined agreements. Rather, the proposed model employs a simple grim trigger strategy where a CP is threatened by the elimination of future VM hosting by other CPs. Simulation results demonstrate the performance of the proposed model in terms of the increased profit and the reduction in the variance in the spot market VM availability and prices.
Binding mechanism of PicoGreen to DNA characterized by magnetic tweezers and fluorescence spectroscopy
(2017)

Fluorescent dyes are broadly used in many biotechnological applications to detect and visualize DNA molecules. However, their binding to DNA alters the structural and nanomechanical properties of DNA and, thus, interferes with associated biological processes. In this work we employed magnetic tweezers and fluorescence spectroscopy to investigate the binding of PicoGreen to DNA at room temperature in a concentration-dependent manner. PicoGreen is an ultrasensitive quinolinium nucleic acid stain exhibiting hardly any background signal from unbound dye molecules. By means of stretching and overwinding single, torsionally constrained, nick-free double-stranded DNA molecules, we acquired force-extension and supercoiling curves which allow quantifying DNA contour length, persistence length and other thermodynamical binding parameters, respectively. The results of our magnetic tweezers single-molecule binding study were well supported through analyzing the fluorescent spectra of stained DNA. On the basis of our work, we could identify a concentration-dependent bimodal binding behavior, where, apparently, PicoGreen associates to DNA as an intercalator and minor-groove binder simultaneously.
Applications of Random Sampling in Computational Geometry, II
(1988)

Random sampling is used for several new geometric algorithms. The algorithms are “Las Vegas,” and their expected bounds are with respect to the random behavior of the algorithms. One algorithm reports all the intersecting pairs of a set of line segments in the plane, and requires <italic>&Ogr;</italic>(<italic>A</italic> + <italic>n</italic> log <italic>n</italic>) expected time, where <italic>A</italic> is the size of the answer, the number of intersecting pairs reported. The algorithm requires <italic>&Ogr;</italic>(<italic>n</italic>) space in the worst case. Another algorithm computes the convex hull of a point set in <italic>E</italic><supscrpt>3</supscrpt> in <italic>&Ogr;</italic>(<italic>n</italic> log <italic>A</italic>) expected time, where <italic>n</italic> is the number of points and <italic>A</italic> is the number of points on the surface of the hull. A simple Las Vegas algorithm triangulates simple polygons in <italic>&Ogr;</italic>(<italic>n</italic> log log <italic>n</italic>) expected time. Algorithms for half-space range reporting are also given. In addition, this paper gives asymptotically tight bounds for a combinatorial quantity of interest in discrete and computational geometry, related to halfspace partitions of point sets.
Comparison of Different Classification Techniques Using WEKA for Breast Cancer
(2007)

The development of data-mining applications such as classification and clustering has shown the need for machine learning algorithms to be applied to large scale data. In this paper we present the comparison of different classification techniques using Waikato Environment for Knowledge Analysis or in short, WEKA. WEKA is an open source software which consists of a collection of machine learning algorithms for data mining tasks. The aim of this paper is to investigate the performance of different classification or clustering methods for a set of large data. The algorithm or methods tested are Bayes Network, Radial Basis Function, Pruned Tree, Single Conjunctive Rule Learner and Nearest Neighbors Algorithm. A fundamental review on the selected technique is presented for introduction purposes. The data breast cancer data with a total data of 6291 and a dimension of 699 rows and 9 columns will be used to test and justify the differences between the classification methods or algorithms. Subsequently, the classification technique that has the potential to significantly improve the common or conventional methods will be suggested for use in large scale data, bioinformatics or other general applications. Keywords— Machine Learning, Data Mining, WEKA, Classification, Bioinformatics.
Classification of Liver Cancer using Artificial Neural Network and Support Vector Machine
(2014)

The purpose of this study is to compare the performance of Artificial Neural Network (ANN) and Support Vector Machine (SVM) for liver cancer classification. The performance of both models is compared and validated on BUPA Liver Disorder Dataset in terms of accuracy, sensitivity, specificity and Area under Curve (AUC). The comparative results show that the SVM classifier outperforms ANN classifier where SVM gives an accuracy of 63.11%, specificity of 100% and AUC of 68.34% where as ANN gives classification accuracy of 57.28%, specificity of 32.56 and AUC of 53.78. This result indicates that the classification capability of SVM is better than ANN and may potentially fill in a critical gap in the use of current or future classification algorithms for liver cancer.
A simulation study of the number of events per variable in logistic regression analysis.
(1996)

We performed a Monte Carlo study to evaluate the effect of the number of events per variable (EPV) analyzed in logistic regression analysis. The simulations were based on data from a cardiac trial of 673 patients in which 252 deaths occurred and seven variables were cogent predictors of mortality; the number of events per predictive variable was (252/7 =) 36 for the full sample. For the simulations, at values of EPV = 2, 5, 10, 15, 20, and 25, we randomly generated 500 samples of the 673 patients, chosen with replacement, according to a logistic model derived from the full sample. Simulation results for the regression coefficients for each variable in each group of 500 samples were compared for bias, precision, and significance testing against the results of the model fitted to the original sample. For EPV values of 10 or greater, no major problems occurred. For EPV values less than 10, however, the regression coefficients were biased in both positive and negative directions; the large sample variance estimates from the logistic model both overestimated and underestimated the sample variance of the regression coefficients; the 90% confidence limits about the estimated values did not have proper coverage; the Wald statistic was conservative under the null hypothesis; and paradoxical associations (significance in the wrong direction) were increased. Although other factors (such as the total number of events, or sample size) may influence the validity of the logistic model, our findings indicate that low EPV can lead to major problems.
The Role of ICT to Achieve the UN Sustainable Development Goals (SDG)
(2016)

This paper is aiming at illustrating the potential of ICT for achieving the Sustainable Development Goals which were declared by the United Nations in 2015 as binding for all nations of our planet addressing both developing and developed countries. ICT must play a significant role if the SDGs should be achieved as projected in 2030. The paper gives an overview of some of the existing efforts in this area and is written as an appeal to all professionals, scientists and IT-professional and their organization to take a holistic approach for all ICT-activities and projects to always include and monitor the effects of their work on the SDGs. The impacts of ICT on sustainability are twofold: On the one hand there might be negative effects on sustainability such as the generation of electronic waste, on the other hand ICT is definitely an enabler to more efficient resource usage, education and business operations which is critical success factor for achieving the SDGs.
Multi-agent Deep Reinforcement Learning with Extremely Noisy Observations
(2018)

Multi-agent reinforcement learning systems aim to provide interacting agents with the ability to collaboratively learn and adapt to the behaviour of other agents. In many real-world applications, the agents can only acquire a partial view of the world. Here we consider a setting whereby most agents’ observations are also extremely noisy, hence only weakly correlated to the true state of the environment. Under these circumstances, learning an optimal policy becomes particularly challenging, even in the unrealistic case that an agent’s policy can be made conditional upon all other agents’ observations. To overcome these difficulties, we propose a multi-agent deep deterministic policy gradient algorithm enhanced by a communication medium (MADDPG-M), which implements a two-level, concurrent learning mechanism. An agent’s policy depends on its own private observations as well as those explicitly shared by others through a communication medium. At any given point in time, an agent must decide whether its private observations are sufficiently informative to be shared with others. However, our environments provide no explicit feedback informing an agent whether a communication action is beneficial, rather the communication policies must also be learned through experience concurrently to the main policies. Our experimental results demonstrate that the algorithm performs well in six highly non-stationary environments of progressively higher complexity, and offers substantial performance gains compared to the baselines.
Modified GDI Technique-A Power Efficient Method For Digital Circuit Design
(2013)

This paper presents logic style compariso ns based on different logic functions and claimed m odified Gate Diffusion Input logic (Mod-GDI) to be much more power-efficient than Gate Diffusion Input logic (GDI) and complementary CMOS logic design. However, DC and Tr ansient analysis performed on more efficient modifi ed Gate Diffusion Input logic (Mod-GDI) circuit realization s and a wider range of different logic cells, as we ll as the use of practical circuit arrangements reveal Mod-GDI to be superior to GDI and CMOS in the majority cases wit h respect to speed, area, power dissipation, and power-delay pro ducts. This manuscript shows that Mod-GDI is the logic style of preference for the realization of arbitrary combinational circuits, if low voltage, low power, and small power-delay products are of concern. All Simulations are perfor med through PSPICE based on 0.18 μm CMOS technology, and results show power characteristics of Mod-GDI technique of low power digital circuit design. Simulation results shows up to 45% reduction in power-delay product in Mod-GDI. Mod-GDI approach allows realization of a broad variety of multifaceted logic functions by means of only two transistors. T his technique is appropriate for designing of fast, low power circuits, using reduced number of transistor (as compared to CMOS techniques), while improving power characteris tics.
Automated Classification of L/R Hand Movement EEG Signals using Advanced Feature Extraction and Machine Learning
(2013)

In this paper, we propose an automated computer platform for the purpose of classifying Electroencephalography (EEG) signals associated with left and right hand movements using a hybrid system that uses advanced feature extraction techniques and machine learning algorithms. It is known that EEG represents the brain activity by the electrical voltage fluctuations along the scalp, and Brain-Computer Interface (BCI) is a device that enables the use of the brain’s neural activity to communicate with others or to control machines, artificial limbs, or robots without direct physical movements. In our research work, we aspired to find the best feature extraction method that enables the differentiation between left and right executed fist movements through various classification algorithms. The EEG dataset used in this research was created and contributed to PhysioNet by the developers of the BCI2000 instrumentation system. Data was preprocessed using the EEGLAB MATLAB toolbox and artifacts removal was done using AAR. Data was epoched on the basis of Event-Related (De) Synchronization (ERD/ERS) and movement-related cortical potentials (MRCP) features. Mu/beta rhythms were isolated for the ERD/ERS analysis and delta rhythms were isolated for the MRCP analysis. The Independent Component Analysis (ICA) spatial filter was applied on related channels for noise reduction and isolation of both artifactually and neutrally generated EEG sources. The final feature vector included the ERD, ERS, and MRCP features in addition to the mean, power and energy of the activations of the resulting Independent Components (ICs) of the epoched feature datasets. The datasets were inputted into two machinelearning algorithms: Neural Networks (NNs) and Support Vector Machines (SVMs). Intensive experiments were carried out and optimum classification performances of 89.8 and 97.1 were obtained using NN and SVM, respectively. This research shows that this method of feature extraction holds some promise for the classification of various pairs of motor movements, which can be used in a BCI context to mentally control a computer or machine. Keywords—EEG; BCI; ICA; MRCP; ERD/ERS; machine learning; NN; SVM
UMBC_EBIQUITY-CORE: Semantic Textual Similarity Systems
(2013)

We describe three semantic text similarity systems developed for the *SEM 2013 STS shared task and the results of the corresponding three runs. All of them shared a word similarity feature that combined LSA word similarity and WordNet knowledge. The first, which achieved the best mean score of the 89 submitted runs, used a simple term alignment algorithm augmented with penalty terms. The other two runs, ranked second and fourth, used support vector regression models to combine larger sets of features.
Modular Construction of Time-Delay Neural Networks for Speech Recognition
(1989)

Several strategies are described that overcome limitations of basic network models as steps towards the design of large connectionist speech recognition systems. The two major areas of concern are the problem of time and the problem of scaling. Speech signals continuously vary over time and encode and transmit enormous amounts of human knowledge. To decode these signals, neural networks must be able to use appropriate representations of time and it must be possible to extend these nets to almost arbitrary sizes and complexity within finite resources. The problem of time is addressed by the development of a Time-Delay Neural Network; the problem of scaling by Modularity and Incremental Design of large nets based on smaller subcomponent nets. It is shown that small networks trained to perform limited tasks develop time invariant, hidden abstractions that can subsequently be exploited to train larger, more complex nets efficiently. Using these techniques, phoneme recognition networks of increasing complexity can be constructed that all achieve superior recognition performance.